optimization of light interception, leaf area and yield in “wa38”: comparisons among training systems, rootstocks and pruning techniques

Clicks: 239
ID: 136616
2020
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #35 of 46 articles by views in drinking water engineering and science

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
As apple orchards have transitioned to high-density plantings, proper training systems are required to manage increased leaf area. Leaf area index (LAI) is defined as the ratio between leaf area to ground area (m2/m2) and can infer orchard health, light relationships and productivity. New technologies enable rapid assessments of LAI and light interception (LI) in the orchard. In this study, LAI, LI, and productivity were assessed across two training systems (Spindle and V), two rootstocks (Geneva 41® (G41) and Malling 9—Nic29 (Nic29)) and two pruning techniques (“click” and bending) in 2016 and 2017. The objective of this study was to determine a management strategy for “WA38” to meet optimal levels for LAI (1.2–2.0) and light interception (65–75%). Higher light interception was measured in V compared to Spindle and in G41 compared to Nic29 in both years. Minimal differences in LAI and light interception were detected across pruning techniques. In “WA38” the “click” technique maintained more consistent yields than bending. In both years, the Spindle-Nic29-“click” combination maintained optimal thresholds for LAI (1.93 and 1.48), light interception (66% and 68%) and consistent yields. This sequence helps mitigate “blind wood” and alternate bearing, while optimizing leaf area and light in “WA38”.
Reference Key
anthony2020agronomyoptimization Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Brendon Anthony;Sara Serra;Stefano Musacchi
Journal drinking water engineering and science
Year 2020
DOI
10.3390/agronomy10050689
URL
https://www.mdpi.com/2073-4395/10/5/689
Keywords
leaf area index

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.